Performance analysis of artificial neural network and K Nearest neighbors image classification techniques with wavelet features

In present day classification of multi class image play an important role in engineering and computer vision application like image processing in biomedicai, retrieval of content based image. From some past years researchers and scientists have made a lot of efforts in implementation of an advanced image classification approaches [5, 6, 7, 8, 9, and 10]. The classification of images is a challenging and important task nowadays. In this propose method our objective is to successfully classify an image from given large image data base. Image features which contained most important information for successful classification is extract by using Haar wavelet and Daubechies wavelet (db4) wavelet discrete Mayer wavelet (demy). In this proposed method received image features are first used with ANN for training and testing and then used same image features of different wavelet transform for KNN training testing. Finally we evaluate the performance of both ANN and KNN classifier with different wavelet Features. Highest classification efficiency is received with Dmey based ANN classifier. Proposed work shows an new application and its directly contributes towards image classification. The complete work is experimented in Mat lab 201 1b using real world dataset.